This relates to space-time coding, and more particularly, to channel estimation in space-time coding arrangements.
Space-Time coding (STC) is a powerful wireless transmission technology that enables joint optimized designs of modulation, coding, and transmit diversity modules on wireless links. A key feature of STC is that channel knowledge is not required at the transmitter. While several non-coherent STC schemes have been invented that also do not require channel information at the receiver, they suffer performance penalties relative to coherent techniques. Such non-coherent techniques are therefore more suitable for rapidly fading channels that experience significant variation with the transmission block. However, for quasi static or slowly varying fading channels, training-based channel estimation at the receiver is commonly employed, because it offers better performance.
For single transmit antenna situations, it is known that a training sequence can be constructed that achieves a channel estimation with minimum mean squared error (optimal sequences) by selecting symbols from an Nth root-of-unit alphabet of symbols
      e                  i        ⁢                                  ⁢        2        ⁢        π        ⁢                                  ⁢        k            N        ,      k    =    0    ,  1  ,  2  ,      …    ⁢                  ⁢          (              N        -        1            )        ,when the alphabet size N is not constrained. Such sequences are the Perfect Roots-of-Unity Sequences (PRUS) that have been proposed in the literature, for example, by W. H. Mow, “Sequence Design for Spread Spectrum,” The Chinese University Press, Chinese University of Hong Kong. 1995. The training sequence length, Nt, determines the smallest possible alphabet size. Indeed, it has been shown that for any given length Nt, there exists a PRUS with alphabet size N=2 Nt, and that for some values of Nt smaller alphabet sizes are possible. It follows that a PRUS of a predetermined length might employ a constellation that is other than a “standard” constellation, where a “standard” constellation is one that has a power of 2 number of symbols. Binary phase shift keying (BPSK), quadrature phase shift keying (QPSK), and 8-point phase shift keying (8-PSK) are examples of a standard constellation. Most, if not all, STC systems employ standard constellations for the transmission of information.
Another known approach for creating training sequences constrains the training sequence symbols to a specific (standard) constellation, typically, BPSK, QPSK, or 8-PSK in order that the transmitter and receiver implementations would be simpler (a single mapper in the transmitter and an inverse mapper in the receiver—rather than two). In such a case, however, optimal sequences do not exist for all training lengths Nt. Instead, exhaustive searches must be carried out to identify sub-optimal sequences according to some performance criteria. Alas, such searches may be computationally prohibitive. For example, in the third generation TDMA proposal that is considered by the industry, 8-PSK constellation symbols are transmitted in a block that includes 116 information symbols, and 26 training symbols (Nt=26). No optimal training sequence exists for this value of Nt and constellation size and number of channel taps to estimate, L.
When, for example, two transmit antennas are employed, a training sequence is needed for each antenna, and ideally, the sequences should be uncorrelated. One known way to arrive at such sequences is through an exhaustive search in the sequences space. This space can be quite large. For example, when employing two antennas, and a training sequence of 26 symbols for each antenna, this space contains 82×26 sequences. For current computational technology, this is a prohibitively large space for exhaustive searching. Reducing the constellation of the training sequence to BPSK (from 8-PSK) reduces the search to 22×26 sequences, but that is still quite prohibitively large; and the reduction to a BPSK sequence would increase the achievable mean squared error. Moreover, once the two uncorrelated sequences are found, a generator is necessary for each of the sequences, resulting in an arrangement (for a two antenna case) as shown in FIG. 1, which includes transmitter 10 that includes information encoder 13 that feeds constellation mapper 14 that drives antennas 11 and 12 via switches 15 and 16. To provide training sequences, transmitter 10 includes sequence generator 5 followed by constellation mapper 6 that feeds antenna 11 via switch 15, and sequence generator 7 followed by constellation mapper 8 that feeds antenna 12 via switch 16.